(Near) Real-time Data Analysis for Smart Sensor Shorts in Football

Most injuries in football occur in the lower extremities due to high muscle stress. To prevent such injuries, the Dutch Football Association (KNVB) and the Delft University of Technology developed the Smart Sensor Shorts, an inertial sensor-based tracking system measuring the athlete’s lower body kinematics, to improve physical load estimates...

Football activity recognition: Improving and testing football activity recognition based on signal data using deep learning.

There is a raising demand for player statistics in the world of football. With the developments over the last years in wearable sensors, Human Activity Recognition (HAR) based on wearable IMU sensors can be used to tackle this problem. This thesis builds upon an earlier research done for this topic, where an end-to-end pipeline based on deep...

Real-time Ball-touch Classification on an Insole Sensor using Neural Networks

Monitoring and analyzing human movement is used in many fields, ranging from healthcare and industrial applications to sports analytics. To provide a football player or their coach with insight into their performance during a game, or their technical development over time, many methods are available such as camera setups and smart vests. However...

An Artificial Neural Network Approach to Within-Game Predictions in Football using Spatiotemporal Data

Recent developments in \ac{ML} have paved the way for unprecedented possibilities in the field of data analytics in numerous team sports, such as American football, baseball, and basketball. In more recent years, \ac{ML} techniques have been applied to football as professional teams got inspired to collect enormous quantities of data to evaluate...

An End-to-End Deep Learning Pipeline for Football Activity Recognition Based on Wearable Acceleration Sensors

Action statistics in sports, such as the number of sprints and jumps, along with the details of the corresponding locomotor actions, are of high interest to coaches and players, as well as medical staff. Current video-based systems have the disadvantage that they are costly and not easily transportable to new locations. In this study, we...

Where to Score in AI World Cup Football?

To push the boundaries of technology, the world cup football for robots, RoboCup, is organized on a yearly basis since 1997. To push the boundaries of artificial intelligence, a simulated version of the RoboCup, AI World Cup Football, is arranged yearly from 2017. This requires skillful attackers, defenders and goalkeeper. A large part of having...

Defensive Positioning Strategies For A Goalkeeper In The AI World Cup

The AI World Cup is a virtual competition in which teams of five players compete in a football match. The defensive strategies for the goalkeeper in this environment are yet to be researched, however. In previous editions of the competition the participating teams use a basic goalkeeper that can only dive but not position itself. This project...

Football activity recognition: A deep learning approach to football activity recognition based on Inertial Measurement Units signals

been given to Human Activity Recognition (HAR) based on signals obtained by IMUs placed on different body parts. This thesis studies the usage of Deep Learning-based models to recognize different football activities in an accurate, robust, and fast manner. Several deep architectures were trained with data captured with IMU sensors placed on...

Classification in football: Activity classification using sensor data in football

The goal of this report is to present and describe the effort surrounding the completion of the Master Thesis Project of classification in football. Classification is a procedure which belongs in the field of Statistics. The objective is to capture, detect and distinguish certain actions relative to the environment of analysis. In our case we are...

Predicting Football Outcomes: with Bayesian Networks

In this thesis Bayesian Networks are used to predict European football matches between the years 2008 and 2016. The goal of this research is to see how the structures learned by different Bayesian Network learning algorithms influences the predictions. First the data is explored and modified to be used for Bayesian Networks and secondly the...

Monitoring local muscle load in football: Use leg acceleration, processed with a big data analysis approach, as an indication of the local muscle load to accurately represent the players’ experienced load

In football, a lot of hip and thigh muscle injuries occur as a result of high muscular loads due to accelerative leg movements. To prevent muscle damage and optimise performance, it is essential to continuously identify when and how frequent local hip and thigh muscular loads develop in the explosive and dynamic football environment. The...

Investigation of Footballs Wakes by Robotic Volumetric PIV

The robotic volumetric PIV system of the TU Delft is proposed to be used to gather quantitative data in the wakes of footballs. For over a decade now, a new football is introduced every FIFA World Cup. The varying surface characteristics affect the behaviour of the footballs in flight, and so football aerodynamics has become subject to increased...